Discovering Hierarchical Neural Archetype Sets. - Gabriele Ciravegna; Pietro Barbiero; Giansalvo Cirrincione; Giovanni Squillero; Alberto Tonda (IIH-MSP (1), 2017)

Understanding Abstraction in Deep CNN: An Application on Facial Emotion Recognition. - Francesca Nonis; Pietro Barbiero; Giansalvo Cirrincione; Elena Carlotta Olivetti; Federica Marcolin; Enrico Vezzetti (IIH-MSP (1), 2017)

A Novel Outlook on Feature Selection as a Multi-objective Problem. - Pietro Barbiero; Evelyne Lutton; Giovanni Squillero; Alberto Paolo Tonda (EA, 2019)

Fundamental Flowers: Evolutionary Discovery of Coresets for Classification. - Pietro Barbiero; Alberto Paolo Tonda (EvoApplications, 2019)

Beyond coreset discovery: evolutionary archetypes. - Pietro Barbiero; Giovanni Squillero; Alberto Paolo Tonda (GECCO (Companion), 2019)

Evolutionary discovery of coresets for classification. - Pietro Barbiero; Giovanni Squillero; Alberto Paolo Tonda (GECCO (Companion), 2019)

Unsupervised Gene Identification in Colorectal Cancer. - Pietro Barbiero; Andrea Bertotti; Gabriele Ciravegna; Giansalvo Cirrincione; Eros Pasero; Elio Piccolo (Quantifying and Processing Biomedical and Behavioral Signals, 2019)

Supervised Gene Identification in Colorectal Cancer. - Pietro Barbiero; Andrea Bertotti; Gabriele Ciravegna; Giansalvo Cirrincione; Eros Pasero; Elio Piccolo (Quantifying and Processing Biomedical and Behavioral Signals, 2019)

Unsupervised Multi-omic Data Fusion: The Neural Graph Learning Network. - Pietro Barbiero; Marta Lovino; Mattia Siviero; Gabriele Ciravegna; Vincenzo Randazzo; Elisa Ficarra; Giansalvo Cirrincione (ICIC (1), 2020)

Towards Uncovering Feature Extraction From Temporal Signals in Deep CNN: the ECG Case Study. - Jacopo Ferretti; Pietro Barbiero; Vincenzo Randazzo; Giansalvo Cirrincione; Eros Pasero (IJCNN, 2020)

Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms. - Pietro Barbiero; Giovanni Squillero; Alberto Paolo Tonda (CoRR, 2020)

The Computational Patient has Diabetes and a COVID. - Pietro Barbiero; Pietro Lió (CoRR, 2020)

Modeling Generalization in Machine Learning: A Methodological and Computational Study. - Pietro Barbiero; Giovanni Squillero; Alberto Paolo Tonda (CoRR, 2020)

Topological Gradient-based Competitive Learning. - Pietro Barbiero; Gabriele Ciravegna; Vincenzo Randazzo; Giansalvo Cirrincione (CoRR, 2020)

Gradient-based Competitive Learning: Theory. - Giansalvo Cirrincione; Pietro Barbiero; Gabriele Ciravegna; Vincenzo Randazzo (CoRR, 2020)

Graph representation forecasting of patient's medical conditions: towards a digital twin. - Pietro Barbiero; Ramón Viñas Torné 001; Pietro Lió (CoRR, 2020)

The GH-EXIN neural network for hierarchical clustering. - Giansalvo Cirrincione; Gabriele Ciravegna; Pietro Barbiero; Vincenzo Randazzo; Eros Pasero (Neural Networks, 2020)

DNA Microarray Classification: Evolutionary Optimization of Neural Network Hyper-parameters. - Pietro Barbiero; Andrea Bertotti; Gabriele Ciravegna; Giansalvo Cirrincione; Elio Piccolo (Neural Approaches to Dynamics of Signal Exchanges, 2020)

Understanding Cancer Phenomenon at Gene Expression Level by using a Shallow Neural Network Chain. - Pietro Barbiero; Andrea Bertotti; Gabriele Ciravegna; Giansalvo Cirrincione; Elio Piccolo; Alberto Tonda (Neural Approaches to Dynamics of Signal Exchanges, 2020)

Neural Epistemology in Dynamical System Learning. - Pietro Barbiero; Giansalvo Cirrincione; Maurizio Cirrincione; Elio Piccolo; Francesco Vaccarino (Neural Approaches to Dynamics of Signal Exchanges, 2020)

Assessing Discriminating Capability of Geometrical Descriptors for 3D Face Recognition by Using the GH-EXIN Neural Network. - Gabriele Ciravegna; Giansalvo Cirrincione; Federica Marcolin; Pietro Barbiero; Nicole Dagnes; Elio Piccolo (Neural Approaches to Dynamics of Signal Exchanges, 2020)

pietrobarbiero/computational-patient: Absolutno. - Pietro Barbiero; Pietro Liò (Zenodo, 2020)

pietrobarbiero/digital-patient: Absolutno. - Pietro Barbiero; Ramón Viñas Torné 001; Pietro Liò (Zenodo, 2020)

Topological Gradient-based Competitive Learning. - Pietro Barbiero; Gabriele Ciravegna; Vincenzo Randazzo; Eros Pasero; Giansalvo Cirrincione (IJCNN, 2021)

Predictable Features Elimination: An Unsupervised Approach to Feature Selection. - Pietro Barbiero; Giovanni Squillero; Alberto Tonda (LOD, 2021)

LENs: a Python library for Logic Explained Networks. - Pietro Barbiero; Gabriele Ciravegna; Dobrik Georgiev; Francesco Giannini (CoRR, 2021)

Entropy-Based Logic Explanations of Neural Networks. - Pietro Barbiero; Gabriele Ciravegna; Francesco Giannini; Pietro Lió; Marco Gori; Stefano Melacci (AAAI, 2022)

Algorithmic Concept-Based Explainable Reasoning. - Dobrik Georgiev; Pietro Barbiero; Dmitry Kazhdan; Petar Velickovic; Pietro Lió (AAAI, 2022)

Extending Logic Explained Networks to Text Classification. - Rishabh Jain; Gabriele Ciravegna; Pietro Barbiero; Francesco Giannini; Davide Buffelli; Pietro Liò (EMNLP, 2022)

Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off. - Mateo Espinosa Zarlenga; Pietro Barbiero; Gabriele Ciravegna; Giuseppe Marra; Francesco Giannini; Michelangelo Diligenti; Zohreh Shams; Frédéric Precioso; Stefano Melacci; Adrian Weller; Pietro Lió; Mateja Jamnik (NeurIPS, 2022)

Encoding Concepts in Graph Neural Networks. - Lucie Charlotte Magister; Pietro Barbiero; Dmitry Kazhdan; Federico Siciliano; Gabriele Ciravegna; Fabrizio Silvestri; Pietro Liò; Mateja Jamnik (CoRR, 2022)

Concept Embedding Models. - Mateo Espinosa Zarlenga; Pietro Barbiero; Gabriele Ciravegna; Giuseppe Marra; Francesco Giannini; Michelangelo Diligenti; Zohreh Shams; Frédéric Precioso; Stefano Melacci; Adrian Weller; Pietro Liò; Mateja Jamnik (CoRR, 2022)

A survey on data integration for multi-omics sample clustering. - Marta Lovino; Vincenzo Randazzo; Gabriele Ciravegna; Pietro Barbiero; Elisa Ficarra; Giansalvo Cirrincione (Neurocomputing, 2022)

Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis. - Han Xuanyuan; Pietro Barbiero; Dobrik Georgiev; Lucie Charlotte Magister; Pietro Liò (AAAI, 2023)

Towards Robust Metrics for Concept Representation Evaluation. - Mateo Espinosa Zarlenga; Pietro Barbiero; Zohreh Shams; Dmitry Kazhdan; Umang Bhatt; Adrian Weller; Mateja Jamnik (AAAI, 2023)

Enhancing XGBoost with Heuristic Smoothing for Transportation Mode and Activity Recognition. - Lidia Alecci; Leonardo Alchieri; Nouran Abdalazim; Pietro Barbiero; Silvia Santini; Martin Gjoreski (UbiComp/ISWC Adjunct, 2023)

Global Explainability of GNNs via Logic Combination of Learned Concepts. - Steve Azzolin; Antonio Longa; Pietro Barbiero; Pietro Liò; Andrea Passerini (ICLR, 2023)

Interpretable Neural-Symbolic Concept Reasoning. - Pietro Barbiero; Gabriele Ciravegna; Francesco Giannini; Mateo Espinosa Zarlenga; Lucie Charlotte Magister; Alberto Tonda; Pietro Lio; Frédéric Precioso; Mateja Jamnik; Giuseppe Marra (ICML, 2023)

From charts to atlas: Merging latent spaces into one. - Donato Crisostomi; Irene Cannistraci; Luca Moschella; Pietro Barbiero; Marco Ciccone; Pietro Liò; Emanuele Rodolà (NeurReps, 2023)

Interpretable Graph Networks Formulate Universal Algebra Conjectures. - Francesco Giannini; Stefano Fioravanti; Oguzhan Keskin; Alisia Maria Lupidi; Lucie Charlotte Magister; Pietro Lió; Pietro Barbiero (NeurIPS, 2023)

Bridging Equational Properties and Patterns on Graphs: an AI-Based Approach. - Oguzhan Keskin; Alisia Maria Lupidi; Stefano Fioravanti; Lucie Charlotte Magister; Pietro Barbiero; Pietro Lio; Francesco Giannini (TAG-ML, 2023)

Explainable Depression Detection Using Handwriting Features. - Francesco Prinzi; Gennaro Raimo; Pietro Barbiero; Gennaro Cordasco; Pietro Liò; Salvatore Vitabile; Anna Esposito (WIRN, 2023)

Concept Distillation in Graph Neural Networks. - Lucie Charlotte Magister; Pietro Barbiero; Dmitry Kazhdan; Federico Siciliano; Gabriele Ciravegna; Fabrizio Silvestri; Mateja Jamnik; Pietro Liò (xAI (3), 2023)

Logic Explained Networks. - Gabriele Ciravegna; Pietro Barbiero; Francesco Giannini; Marco Gori; Pietro Liò; Marco Maggini; Stefano Melacci (Artif. Intell., 2023)

GCI: A (G)raph (C)oncept (I)nterpretation Framework. - Dmitry Kazhdan; Botty Dimanov; Lucie Charlotte Magister; Pietro Barbiero; Mateja Jamnik; Pietro Liò (CoRR, 2023)

Categorical Foundations of Explainable AI: A Unifying Formalism of Structures and Semantics. - Pietro Barbiero; Stefano Fioravanti; Francesco Giannini; Alberto Tonda; Pietro Liò; Elena Di Lavore (CoRR, 2023)

SHARCS: Shared Concept Space for Explainable Multimodal Learning. - Gabriele Dominici; Pietro Barbiero; Lucie Charlotte Magister; Pietro Liò; Nikola Simidjievski (CoRR, 2023)

Relational Concept Based Models. - Pietro Barbiero; Francesco Giannini; Gabriele Ciravegna; Michelangelo Diligenti; Giuseppe Marra (CoRR, 2023)

Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts. - Jonas Jürß; Lucie Charlotte Magister; Pietro Barbiero; Pietro Liò; Nikola Simidjievski (CoRR, 2023)

Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians. - Alessandro Farace di Villaforesta; Lucie Charlotte Magister; Pietro Barbiero; Pietro Liò (CoRR, 2023)

Learning Logic Explanations by Neural Networks. - Gabriele Ciravegna; Francesco Giannini; Pietro Barbiero; Marco Gori; Pietro Lio; Marco Maggini; Stefano Melacci (Compendium of Neurosymbolic Artificial Intelligence, 2023)

Generative Models for Counterfactual Explanations. - Daniil E. Kirilenko; Pietro Barbiero; Martin Gjoreski; Mitja Lustrek; Marc Langheinrich (HI-AI@KDD, 2024)

Workshop on Human-Interpretable AI. - Gabriele Ciravegna; Mateo Espinosa Zarlenga; Pietro Barbiero; Francesco Giannini; Zohreh Shams; Damien Garreau; Mateja Jamnik; Tania Cerquitelli (KDD, 2024)

Relational Concept Bottleneck Models. - Pietro Barbiero; Francesco Giannini; Gabriele Ciravegna; Michelangelo Diligenti; Giuseppe Marra (NeurIPS, 2024)

Interpretable Concept-Based Memory Reasoning. - David Debot; Pietro Barbiero; Francesco Giannini; Gabriele Ciravegna; Michelangelo Diligenti; Giuseppe Marra (NeurIPS, 2024)

Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning. - Dario Fenoglio; Gabriele Dominici; Pietro Barbiero; Alberto Tonda; Martin Gjoreski; Marc Langheinrich (NeurIPS, 2024)

AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model. - Gabriele Dominici; Pietro Barbiero; Francesco Giannini; Martin Gjoreski; Marc Langheinrich (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2024)

Categorical Foundation of Explainable AI: A Unifying Theory. - Francesco Giannini; Stefano Fioravanti; Pietro Barbiero; Alberto Tonda; Pietro Liò; Elena Di Lavore (xAI (3), 2024)

Climbing the Ladder of Interpretability with Counterfactual Concept Bottleneck Models. - Gabriele Dominici; Pietro Barbiero; Francesco Giannini; Martin Gjoreski; Giuseppe Marra; Marc Langheinrich (CoRR, 2024)

Causal Concept Embedding Models: Beyond Causal Opacity in Deep Learning. - Gabriele Dominici; Pietro Barbiero; Mateo Espinosa Zarlenga; Alberto Termine; Martin Gjoreski; Giuseppe Marra; Marc Langheinrich (CoRR, 2024)

Self-supervised Interpretable Concept-based Models for Text Classification. - Francesco De Santis; Philippe Bich; Gabriele Ciravegna; Pietro Barbiero; Danilo Giordano; Tania Cerquitelli (CoRR, 2024)

Counterfactual Explanations for Clustering Models. - Aurora Spagnol; Kacper Sokol; Pietro Barbiero; Marc Langheinrich; Martin Gjoreski (CoRR, 2024)

Gradient-Based Competitive Learning: Theory. - Giansalvo Cirrincione; Vincenzo Randazzo; Pietro Barbiero; Gabriele Ciravegna; Eros Pasero (Cogn. Comput., 2024)

Counterfactual Concept Bottleneck Models. - Gabriele Dominici; Pietro Barbiero; Francesco Giannini; Martin Gjoreski; Giuseppe Marra; Marc Langheinrich (ICLR, 2025)

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning. - Gabriele Dominici; Pietro Barbiero; Mateo Espinosa Zarlenga; Alberto Termine; Martin Gjoreski; Giuseppe Marra; Marc Langheinrich (ICLR, 2025)

Using AI explainable models and handwriting/drawing tasks for psychological well-being. - Francesco Prinzi; Pietro Barbiero; Claudia Greco; Terry Amorese; Gennaro Cordasco; Pietro Liò; Salvatore Vitabile; Anna Esposito (Inf. Syst., 2025)

A Survey on Federated Learning in Human Sensing. - Mohan Li; Martin Gjoreski; Pietro Barbiero; Gasper Slapnicar; Mitja Lustrek; Nicholas D. Lane; Marc Langheinrich (CoRR, 2025)

Neural Interpretable Reasoning. - Pietro Barbiero; Giuseppe Marra; Gabriele Ciravegna; David Debot; Francesco De Santis; Michelangelo Diligenti; Mateo Espinosa Zarlenga; Francesco Giannini (CoRR, 2025)

Causally Reliable Concept Bottleneck Models. - Giovanni de Felice; Arianna Casanova Flores; Francesco De Santis; Silvia Santini; Johannes Schneider; Pietro Barbiero; Alberto Termine (CoRR, 2025)

Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts. - Andrea Pugnana; Riccardo Massidda; Francesco Giannini; Pietro Barbiero; Mateo Espinosa Zarlenga; Roberto Pellungrini; Gabriele Dominici; Fosca Giannotti; Davide Bacciu (CoRR, 2025)

Logic Explanation of AI Classifiers by Categorical Explaining Functors. - Stefano Fioravanti; Francesco Giannini; Paolo Frazzetto; Fabio Zanasi; Pietro Barbiero (CoRR, 2025)

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts. - Mateo Espinosa Zarlenga; Gabriele Dominici; Pietro Barbiero; Zohreh Shams; Mateja Jamnik (CoRR, 2025)

If Concept Bottlenecks are the Question, are Foundation Models the Answer? - Nicola Debole; Pietro Barbiero; Francesco Giannini; Andrea Passerini; Stefano Teso; Emanuele Marconato (CoRR, 2025)

Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models. - Francesco De Santis; Philippe Bich; Gabriele Ciravegna; Pietro Barbiero; Danilo Giordano; Tania Cerquitelli (CoRR, 2025)

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning. - David Debot; Pietro Barbiero; Gabriele Dominici; Giuseppe Marra (CoRR, 2025)

Foundations of Interpretable Models. - Pietro Barbiero; Mateo Espinosa Zarlenga; Alberto Termine; Mateja Jamnik; Giuseppe Marra (CoRR, 2025)